Investigation into archaeal extremophilic lifestyles through comparative proteogenomic analysis
Bibliographic record
Abstract
Archaea are a group of primary life forms on Earth and could thrive in many unique environments. Their successful colonization of extreme niches requires corresponding adaptations at proteogenomic level in order to maintain stable cellular structures and active physiological functions. Although some studies have already investigated the extremophilic lifestyles of archaeal species based on genomic features and protein structures, there is a lack of comparative proteogenomic analysis in a large scale. In this study, we explored 686 high-quality archaeal genomes (proteomes) sourced from the Pathosystems Resource Integration Center (PATRIC) database. General patterns of genomic features such as genome size, coding capacity (coding genes and non-coding regions), and G + C contents were re-confirmed. Protein domain distribution patterns were then identified across archaeal species. Domains with unknown functions (DUFs) and mini proteins were investigated in terms of their distributions due to their importance in archaeal physiological functions. In addition, physicochemical properties of protein sequences, such as stability, hydrophobicity, isoelectric point, aromaticity and amino acid compositions in corresponding archaeal groups were compared. Unique features associated with extremophilic lifestyles were observed, which suggested that evolutionary adaptations to different extreme environments had intrinsic impacts on archaeal protein features. Taken together, this systematic study facilitates a better understanding of the mechanisms behind the extremophilic lifestyles of archaeal species, which will further contribute to the evolutionary explorations of archaeal adaptations both experimentally and theoretically in the future studies. Communicated by Ramaswamy H. Sarma
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".